CVE-2025-10975 is a medium severity vulnerability affecting the GuanxingLu vlarl product, specifically in the ZeroMQ component within the function experiments.robot.bridge.reasoning_server::run_reasoning_server located in experiments/robot/bridge/reasoning_server.py. The vulnerability arises from unsafe deserialization of the Message argument, which can be manipulated remotely by an attacker to execute arbitrary code or cause denial of service. Deserialization vulnerabilities typically occur when untrusted data is deserialized without proper validation, allowing attackers to craft malicious payloads that execute unintended commands or corrupt memory. The vulnerability is remotely exploitable without user interaction and requires low privileges (PR:L), indicating that an attacker with limited access could exploit it over the network. The product uses a rolling release model, so specific version numbers for patched releases are not provided. The CVSS 4.0 score is 5.3 (medium), reflecting network attack vector, low attack complexity, no user interaction, and partial impacts on confidentiality, integrity, and availability. Although no known exploits are currently in the wild, a public exploit has been published, increasing the risk of exploitation. The vulnerability affects the deserialization process in a component that likely handles inter-process or network communication via ZeroMQ, a messaging library, making it critical to secure message handling to prevent remote code execution or service disruption.

For European organizations, this vulnerability poses a moderate risk, especially for those deploying GuanxingLu vlarl in environments where the reasoning_server component is exposed to untrusted networks or users. Exploitation could lead to unauthorized code execution, data corruption, or denial of service, impacting operational continuity and data integrity. Organizations relying on this product for automation, robotics, or AI reasoning tasks may face disruptions in critical workflows. The medium severity suggests that while the impact is not catastrophic, it could enable lateral movement or serve as an entry point for more severe attacks if combined with other vulnerabilities. European entities in sectors such as manufacturing, research institutions, or technology companies using GuanxingLu vlarl should be particularly vigilant. Additionally, compliance with GDPR and other data protection regulations means that any compromise affecting data confidentiality or integrity could result in regulatory penalties and reputational damage.

To mitigate this vulnerability, European organizations should: 1) Immediately identify and isolate instances of GuanxingLu vlarl running the affected code version. 2) Apply any available patches or updates from the vendor as soon as they are released, despite the rolling release model complicating version tracking; engage with the vendor for precise update guidance. 3) Implement network segmentation and firewall rules to restrict access to the reasoning_server component, limiting exposure to trusted internal networks only. 4) Employ input validation and sanitization on all incoming messages to the ZeroMQ component to prevent malicious payloads from being processed. 5) Monitor logs and network traffic for anomalous activity indicative of exploitation attempts, such as unexpected deserialization errors or unusual message patterns. 6) Consider deploying runtime application self-protection (RASP) or behavior-based intrusion detection systems to detect and block exploitation attempts in real time. 7) Conduct security audits and code reviews focusing on deserialization and message handling practices to identify and remediate similar vulnerabilities proactively.